NEC Labs

Cupertino, United States
Cupertino, United States

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Chen K.,Hong Kong University of Science and Technology | Singla A.,University of Illinois at Urbana - Champaign | Singh A.,NEC Labs | Ramachandran K.,NEC Labs | And 4 more authors.
IEEE/ACM Transactions on Networking | Year: 2014

A detailed examination of evolving traffic characteristics, operator requirements, and network technology trends suggests a move away from nonblocking interconnects in data center networks (DCNs). As a result, recent efforts have advocated oversubscribed networks with the capability to adapt to traffic requirements on-demand. In this paper, we present the design, implementation, and evaluation of OSA, a novel Optical Switching Architecture for DCNs. Leveraging runtime reconfigurable optical devices, OSA dynamically changes its topology and link capacities, thereby achieving unprecedented flexibility to adapt to dynamic traffic patterns. Extensive analytical simulations using both real and synthetic traffic patterns demonstrate that OSA can deliver high bisection bandwidth (60%-100% of the nonblocking architecture). Implementation and evaluation of a small-scale functional prototype further demonstrate the feasibility of OSA. © 2013 IEEE.


Liu N.N.,Hong Kong University of Science and Technology | He L.,Hong Kong University of Science and Technology | Zhao M.,NEC Labs
ACM Transactions on Intelligent Systems and Technology | Year: 2013

Most existing collaborative filtering models only consider the use of user feedback (e.g., ratings) and meta data (e.g., content, demographics). However, in most real world recommender systems, context information, such as time and social networks, are also very important factors that could be considered in order to produce more accurate recommendations. In this work, we address several challenges for the context aware movie recommendation tasks in CAMRa 2010: (1) how to combine multiple heterogeneous forms of user feedback? (2) how to cope with dynamic user and item characteristics? (3) how to capture and utilize social connections among users? For the first challenge, we propose a novel ranking based matrix factorization model to aggregate explicit and implicit user feedback. For the second challenge, we extend this model to a sequential matrix factorization model to enable time-aware parametrization. Finally, we introduce a network regularization function to constrain user parameters based on social connections. To the best of our knowledge, this is the first study that investigates the collective modeling of social and temporal dynamics. Experiments on the CAMRa 2010 dataset demonstrated clear improvements over many baselines. © 2013 ACM.


Kanazawa A.,University of Maryland University College | Jacobs D.W.,University of Maryland University College | Chandraker M.,NEC Labs
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | Year: 2016

We present an approach to matching images of objects in fine-grained datasets without using part annotations, with an application to the challenging problem of weakly supervised single-view reconstruction. This is in contrast to prior works that require part annotations, since matching objects across class and pose variations is challenging with appearance features alone. We overcome this challenge through a novel deep learning architecture, WarpNet, that aligns an object in one image with a different object in another. We exploit the structure of the fine-grained dataset to create artificial data for training this network in an unsupervised-discriminative learning approach. The output of the network acts as a spatial prior that allows generalization at test time to match real images across variations in appearance, viewpoint and articulation. On the CUB-200-2011 dataset of bird categories, we improve the AP over an appearance-only network by 13.6%. We further demonstrate that our WarpNet matches, together with the structure of fine-grained datasets, allow single-view reconstructions with quality comparable to using annotated point correspondences.


Wang Z.,Princeton University | Kravtsov K.S.,Princeton University | Huang Y.-K.,NEC Labs | Prucnal P.R.,Princeton University
Optics Express | Year: 2011

Arrayed waveguide gratings (AWG) are widely used as wavelength division multiplexers (MUX) and demultiplexers (DEMUX) in optical networks. Here we propose and demonstrate that conventional AWGs can also be used as integrated spectral filters to realize a Fast Fourier transform (FFT) and its inverse form (IFFT). More specifically, we point out that the wavelength selection conditions of AWGs when used as wavelength MUX/DEMUX also enable them to perform FFT/IFFT functions. Therefore, previous research on AWGs can now be applied to optical FFT/IFFT circuit design. Compared with other FFT/IFFT optical circuits, AWGs have less structural complexity, especially for a large number of inputs and outputs. As an important application, AWGs can be used in optical OFDM systems. We propose an all-optical OFDM system with AWGs and demonstrate the simulation results. Overall, the AWG provides a feasible solution for all-optical OFDM systems, especially with a large number of optical subcarriers. © 2011 Optical Society of America.


Wang Z.,Princeton University | Fok M.P.,Princeton University | Xu L.,NEC Labs | Chang J.,Princeton University | Prucnal P.R.,Princeton University
Optics Express | Year: 2010

Temporal phase modulation of spread stealth signals is proposed and demonstrated to improve optical steganography transmission privacy. After phase modulation, the temporally spread stealth signal has a more complex spectral-phase-temporal relationship, such that the original temporal profile cannot be restored when only dispersion compensation is applied to the temporally spread stealth signals. Therefore, it increases the difficulty for the eavesdropper to detect and intercept the stealth channel that is hidden under a public transmission, even with a correct dispersion compensation device. The experimental results demonstrate the feasibility of this approach and display insignificant degradation in transmission performance, compared to the conventional stealth transmission without temporal phase modulation. The proposed system can also work without a clock transmission for signal synchronization. Our analysis and simulation results show that it is difficult for the adversary to detect the existence of the stealth transmission, or find the correct phase mask to recover the stealth signals. © 2010 Optical Society of America.


Sharma P.,University of Southern California | Huang C.,NEC Labs | Nevatia R.,University of Southern California
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | Year: 2012

Most common approaches for object detection collect thousands of training examples and train a detector in an offline setting, using supervised learning methods, with the objective of obtaining a generalized detector that would give good performance on various test datasets. However, when an offline trained detector is applied on challenging test datasets, it may fail in some cases by not being able to detect some objects or by producing false alarms. We propose an unsupervised multiple instance learning (MIL) based incremental solution to deal with this issue. We introduce an MIL loss function for Real Adaboost and present a tracking based effective unsupervised online sample collection mechanism to collect the online samples for incremental learning. Experiments demonstrate the effectiveness of our approach by improving the performance of a state of the art offline trained detector on the challenging datasets for pedestrian category. © 2012 IEEE.


Zhou F.,NEC Labs | Lin Y.,NEC Labs
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | Year: 2016

Given a food image, can a fine-grained object recognition engine tell "which restaurant which dish" the food belongs to? Such ultra-fine grained image recognition is the key for many applications like search by images, but it is very challenging because it needs to discern subtle difference between classes while dealing with the scarcity of training data. Fortunately, the ultra-fine granularity naturally brings rich relationships among object classes. This paper proposes a novel approach to exploit the rich relationships through bipartite-graph labels (BGL). We show how to model BGL in an overall convolutional neural networks and the resulting system can be optimized through back-propagation. We also show that it is computationally efficient in inference thanks to the bipartite structure. To facilitate the study, we construct a new food benchmark dataset, which consists of 37,885 food images collected from 6 restaurants and totally 975 menus. Experimental results on this new food and three other datasets demonstrate BGL advances previous works in fine-grained object recognition. An online demo is available at http://www.f-zhou.com/fg-demo/.


Wang T.-C.,University of California at Berkeley | Chandraker M.,NEC Labs | Efros A.A.,University of California at Berkeley | Ramamoorthi R.,University of California at San Diego
Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition | Year: 2016

Light-field cameras have recently emerged as a powerful tool for one-shot passive 3D shape capture. However, obtaining the shape of glossy objects like metals, plastics or ceramics remains challenging, since standard Lambertian cues like photo-consistency cannot be easily applied. In this paper, we derive a spatially-varying (SV)BRDF-invariant theory for recovering 3D shape and reflectance from lightfield cameras. Our key theoretical insight is a novel analysis of diffuse plus single-lobe SVBRDFs under a light-field setup. We show that, although direct shape recovery is not possible, an equation relating depths and normals can still be derived. Using this equation, we then propose using a polynomial (quadratic) shape prior to resolve the shape ambiguity. Once shape is estimated, we also recover the reflectance. We present extensive synthetic data on the entire MERL BRDF dataset, as well as a number of real examples to validate the theory, where we simultaneously recover shape and BRDFs from a single image taken with a Lytro Illum camera.


Kahlon V.,NEC Labs
2012 Formal Methods in Computer-Aided Design, FMCAD 2012 | Year: 2012

Triggering errors in concurrent programs is a notoriously difficult task. A key reason for this is the behavioral complexity resulting from the large number of interleavings of operations of different threads. An even more challenging task is fixing errors once they are detected. In general, automatically synthesizing a correct program from a buggy one is a hard problem. However for simple correctness properties that depend on the syntactic structure of the program rather than its semantics, automatic error correction becomes feasible. In this paper, we consider the problem of lock insertion to enforce critical sections required to fix bugs like atomicity violations. A key challenge in lock insertion is that enforcing critical sections is not the sole criterion that needs to be satisfied. Often other correctness constraints like deadlock-freedom also need to be met. Moreover, apart from ensuring correctness, another key concern during lock insertion is performance. Indeed, mutual exclusion constraints generated by locks kill parallelism thereby impacting performance. Thus it is crucial that the newly introduced critical sections be kept as small as possible. In other words, our goal is lock insertion while meeting the dual, and often conflicting, requirements of (i) correctness and (ii) performance. In this paper, we present a fully automatic, provable optimal, efficient and precise technique for lock insertion in concurrent code that ensures deadlock freedom while attempting to minimize the resulting critical sections. © 2012 IEEE.


Kahlon V.,NEC Labs
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2012

The key to making program analysis practical for large concurrent programs is to isolate a small set of interleavings to be explored without losing precision of the analysis at hand. The state-of-the-art in restricting the set of interleavings while guaranteeing soundness is partial order reduction (POR). The main idea behind POR is to partition all interleavings of the given program into equivalence classes based on the partial orders they induce on shared objects. Then for each partial order at least one interleaving need be explored. POR classifies two interleavings as non-equivalent if executing them leads to different values of shared variables. However, some of the most common properties about concurrent programs like detection of data races, deadlocks and atomicity as well as assertion violations reduce to control state reachability. We exploit the key observation that even though different interleavings may lead to different values of program variables, they may induce the same control behavior. Hence these interleavings, which induce different partial orders, can in fact be treated as being equivalent. Since in most concurrent programs threads are loosely coupled, i.e., the values of shared variables typically flow into a small number of conditional statements of threads, we show that classifying interleavings based on the control behaviors rather than the partial orders they induce, drastically reduces the number of interleavings that need be explored. In order to exploit this loose coupling we leverage the use of dataflow analysis for concurrent programs, specifically numerical domains. This, in turn, greatly enhances the scalability of concurrent program analysis. © 2012 Springer-Verlag Berlin Heidelberg.

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